
"""simple docstring"""






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase        =				[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
					import run_clm_flax
					import run_flax_glue
					import run_flax_ner
					import run_mlm_flax
					import run_qa
					import run_summarization_flax
					import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase        =				logging.getLogger()




def __magic_name__	(				)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  argparse.ArgumentParser()
	parser.add_argument('''-f'''			)
	lowerCAmelCase												=  parser.parse_args()
	return args.f




def __magic_name__	(				_lowerCamelCase:		str,					_lowerCamelCase:		Dict="eval"			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  os.path.join(_lowerCamelCase,					F"""{split}_results.json"""			)
	if os.path.exists(_lowerCamelCase			):
		with open(_lowerCamelCase,					'''r'''			) as f:
			return json.load(_lowerCamelCase			)
	raise ValueError(F"""can't find {path}"""			)


UpperCAmelCase        =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 			lowercase					( lowercase__		):






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_flax_glue.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertGreaterEqual(result['''eval_accuracy''']      ,0.75			)






			@slow
			def 					UpperCAmelCase							(self  :	int			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_clm_flax.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertLess(result['''eval_perplexity''']      ,100			)






			@slow
			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_summarization_flax.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_      ,split='''test'''			)
					self.assertGreaterEqual(result['''test_rouge1''']      ,10			)
					self.assertGreaterEqual(result['''test_rouge2''']      ,2			)
					self.assertGreaterEqual(result['''test_rougeL''']      ,7			)
					self.assertGreaterEqual(result['''test_rougeLsum''']      ,7			)






			@slow
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_mlm_flax.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertLess(result['''eval_perplexity''']      ,42			)






			@slow
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_ta_mlm_flax.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertGreaterEqual(result['''eval_accuracy''']      ,0.42			)






			@slow
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  7 if get_gpu_count() > 1 else 2

				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_flax_ner.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertGreaterEqual(result['''eval_accuracy''']      ,0.75			)
					self.assertGreaterEqual(result['''eval_f1''']      ,0.3			)






			@slow
			def 					UpperCAmelCase							(self  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

				with patch.object(SCREAMING_SNAKE_CASE_      ,'''argv'''      ,SCREAMING_SNAKE_CASE_			):
					run_qa.main()
					lowerCAmelCase												=  get_results(SCREAMING_SNAKE_CASE_			)
					self.assertGreaterEqual(result['''eval_f1''']      ,30			)
					self.assertGreaterEqual(result['''eval_exact''']      ,30			)




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
					from huggingface_hub import snapshot_download
					from pyctcdecode import BeamSearchDecoderCTC

					from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
					from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
					from transformers import WavaVecaForCTC



@require_pyctcdecode
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
				lowerCAmelCase												=  dict(zip(SCREAMING_SNAKE_CASE_      ,range(len(SCREAMING_SNAKE_CASE_			)			)			)			)

				lowerCAmelCase												=  {
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				lowerCAmelCase												=  {
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16_000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

				lowerCAmelCase												=  tempfile.mkdtemp()
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''vocab_file''']			)
				lowerCAmelCase												=  os.path.join(self.tmpdirname      ,SCREAMING_SNAKE_CASE_			)
				with open(self.vocab_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				with open(self.feature_extraction_file      ,'''w'''      ,encoding='''utf-8'''			) as fp:
					fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)

				# load decoder from hub
				lowerCAmelCase												=  '''hf-internal-testing/ngram-beam-search-decoder'''






			def 					UpperCAmelCase							(self  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.add_kwargs_tokens_map.copy()
				kwargs.update(SCREAMING_SNAKE_CASE_			)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				shutil.rmtree(self.tmpdirname			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				processor.save_pretrained(self.tmpdirname			)
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname			)

				# tokenizer
				self.assertEqual(processor.tokenizer.get_vocab()      ,tokenizer.get_vocab()			)
				self.assertIsInstance(processor.tokenizer      ,SCREAMING_SNAKE_CASE_			)

				# feature extractor
				self.assertEqual(processor.feature_extractor.to_json_string()      ,feature_extractor.to_json_string()			)
				self.assertIsInstance(processor.feature_extractor      ,SCREAMING_SNAKE_CASE_			)

				# decoder
				self.assertEqual(processor.decoder._alphabet.labels      ,decoder._alphabet.labels			)
				self.assertEqual(
				    processor.decoder.model_container[decoder._model_key]._unigram_set      ,decoder.model_container[decoder._model_key]._unigram_set      ,)
				self.assertIsInstance(processor.decoder      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM(
				    tokenizer=self.get_tokenizer()      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)
				processor.save_pretrained(self.tmpdirname			)

				# make sure that error is thrown when decoder alphabet doesn't match
				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(
				    self.tmpdirname      ,alpha=5.0      ,beta=3.0      ,score_boundary=-7.0      ,unk_score_offset=3			)

				# decoder
				self.assertEqual(processor.language_model.alpha      ,5.0			)
				self.assertEqual(processor.language_model.beta      ,3.0			)
				self.assertEqual(processor.language_model.score_boundary      ,-7.0			)
				self.assertEqual(processor.language_model.unk_score_offset      ,3			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_tokenizer()
				# add token to trigger raise
				tokenizer.add_tokens(['''xx''']			)
				with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_      ,'''include'''			):
					WavaVecaProcessorWithLM(
					    tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.get_feature_extractor()      ,decoder=self.get_decoder()			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  feature_extractor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()      ,input_processor[key].sum()      ,delta=1e-2			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''This is a test string'''

				lowerCAmelCase												=  processor(text=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer(SCREAMING_SNAKE_CASE_			)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]      ,encoded_processor[key]			)






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=(2, 10, 16)      ,SCREAMING_SNAKE_CASE_  :	List[Any]=77			)       ->	Optional[int]:





				"""simple docstring"""


				np.random.seed(SCREAMING_SNAKE_CASE_			)
				return np.random.rand(*SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits(shape=(10, 16)      ,seed=13			)

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  decoder.decode_beams(SCREAMING_SNAKE_CASE_			)[0]

				self.assertEqual(decoded_decoder[0]      ,decoded_processor.text			)
				self.assertEqual('''</s> <s> </s>'''      ,decoded_processor.text			)
				self.assertEqual(decoded_decoder[-2]      ,decoded_processor.logit_score			)
				self.assertEqual(decoded_decoder[-1]      ,decoded_processor.lm_score			)






			@parameterized.expand([[None], ['''fork'''], ['''spawn''']]			)
			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
				#       otherwise, the LM won't be available to the pool's sub-processes.
				# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
				if pool_context is None:
					lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_			)
				else:
					with get_context(SCREAMING_SNAKE_CASE_			).Pool() as pool:
						lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as p:
					lowerCAmelCase												=  decoder.decode_beams_batch(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  [], [], []
				for beams in decoded_beams:
					texts_decoder.append(beams[0][0]			)
					logit_scores_decoder.append(beams[0][-2]			)
					lm_scores_decoder.append(beams[0][-1]			)

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.text			)
				self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']      ,decoded_processor.text			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.logit_score			)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,decoded_processor.lm_score			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  15
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  -4.0

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,beam_width=SCREAMING_SNAKE_CASE_      ,beam_prune_logp=SCREAMING_SNAKE_CASE_      ,token_min_logp=SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][2] for d in decoded_decoder_out]
				lowerCAmelCase												=  [d[0][3] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']      ,SCREAMING_SNAKE_CASE_			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.logit_score			)			)
				self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)

				self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_      ,decoded_processor_out.lm_score			)			)
				self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  2.0
				lowerCAmelCase												=  5.0
				lowerCAmelCase												=  -20.0
				lowerCAmelCase												=  True

				lowerCAmelCase												=  processor.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  decoded_processor_out.text

				lowerCAmelCase												=  list(SCREAMING_SNAKE_CASE_			)
				decoder.reset_params(
				    alpha=SCREAMING_SNAKE_CASE_      ,beta=SCREAMING_SNAKE_CASE_      ,unk_score_offset=SCREAMING_SNAKE_CASE_      ,lm_score_boundary=SCREAMING_SNAKE_CASE_      ,)

				with get_context('''fork'''			).Pool() as pool:
					lowerCAmelCase												=  decoder.decode_beams_batch(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  [d[0][0] for d in decoded_decoder_out]

				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				self.assertEqual(lm_model.alpha      ,2.0			)
				self.assertEqual(lm_model.beta      ,5.0			)
				self.assertEqual(lm_model.unk_score_offset      ,-20.0			)
				self.assertEqual(lm_model.score_boundary      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ['''alphabet.json''', '''language_model''']

				downloaded_decoder_files.sort()
				expected_decoder_files.sort()

				# test that only decoder relevant files from
				# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
				# are downloaded and none of the rest (e.g. README.md, ...)
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  snapshot_download('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  processor.decoder.model_container[processor.decoder._model_key]
				lowerCAmelCase												=  Path(language_model._kenlm_model.path.decode('''utf-8'''			)			).parent.parent.absolute()

				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  os.listdir(SCREAMING_SNAKE_CASE_			)

				local_decoder_files.sort()
				expected_decoder_files.sort()

				# test that both decoder form hub and local files in cache are the same
				self.assertListEqual(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''			)

				lowerCAmelCase												=  floats_list((3, 1_000)			)

				lowerCAmelCase												=  processor_wavaveca(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)
				lowerCAmelCase												=  processor_auto(SCREAMING_SNAKE_CASE_      ,return_tensors='''np'''			)

				for key in input_wavaveca.keys():
					self.assertAlmostEqual(input_wavaveca[key].sum()      ,input_auto[key].sum()      ,delta=1e-2			)

				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor_wavaveca.batch_decode(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  processor_auto.batch_decode(SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(decoded_wavaveca.text      ,decoded_auto.text			)






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_feature_extractor()
				lowerCAmelCase												=  self.get_tokenizer()
				lowerCAmelCase												=  self.get_decoder()

				lowerCAmelCase												=  WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE_      ,feature_extractor=SCREAMING_SNAKE_CASE_      ,decoder=SCREAMING_SNAKE_CASE_			)

				self.assertListEqual(
				    processor.model_input_names      ,feature_extractor.model_input_names      ,msg='''`processor` and `feature_extractor` model input names do not match'''      ,)






			@staticmethod
			def 					UpperCAmelCase							(SCREAMING_SNAKE_CASE_  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	str			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  [d[key] for d in offsets]
				return retrieved_list






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()[0]

				lowerCAmelCase												=  processor.decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)
				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)			)      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']      ,'''end_offset'''			)      ,[1, 3, 5]			)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''			)
				lowerCAmelCase												=  self._get_dummy_logits()

				lowerCAmelCase												=  processor.batch_decode(SCREAMING_SNAKE_CASE_      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				# check Wav2Vec2CTCTokenizerOutput keys for word
				self.assertEqual(len(outputs.keys()			)      ,4			)
				self.assertTrue('''text''' in outputs			)
				self.assertTrue('''word_offsets''' in outputs			)
				self.assertTrue(isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				self.assertListEqual(
				    [''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			) for o in outputs['''word_offsets''']]      ,outputs.text			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''word'''			)      ,['''<s>''', '''<s>''', '''</s>''']			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''start_offset'''			)      ,[0, 2, 4]			)
				self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]      ,'''end_offset'''			)      ,[1, 3, 5]			)






			@slow
			@require_torch
			@require_torchaudio
			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Any:





				"""simple docstring"""


				import torch

				lowerCAmelCase												=  load_dataset('''common_voice'''      ,'''en'''      ,split='''train'''      ,streaming=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  ds.cast_column('''audio'''      ,datasets.Audio(sampling_rate=16_000			)			)
				lowerCAmelCase												=  iter(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  next(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)
				lowerCAmelCase												=  WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''			)

				# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
				lowerCAmelCase												=  processor(sample['''audio''']['''array''']      ,return_tensors='''pt'''			).input_values

				with torch.no_grad():
					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_			).logits.cpu().numpy()

				lowerCAmelCase												=  processor.decode(logits[0]      ,output_word_offsets=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
				lowerCAmelCase												=  [
				    {
				        '''start_time''': d['''start_offset'''] * time_offset,
				        '''end_time''': d['''end_offset'''] * time_offset,
				        '''word''': d['''word'''],
				    }
				    for d in output['''word_offsets''']
				]

				lowerCAmelCase												=  '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

				# output words
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,SCREAMING_SNAKE_CASE_			)
				self.assertEqual(''' '''.join(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''word'''			)			)      ,output.text			)

				# output times
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''start_time'''			)			)
				lowerCAmelCase												=  torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE_      ,'''end_time'''			)			)

				# fmt: off
				lowerCAmelCase												=  torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]			)
				lowerCAmelCase												=  torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]			)
				# fmt: on

				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)
				self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=0.01			)			)


